Semantic Parsing and Computation Rules for Math Word Problems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing chat models like ChatGPT face challenges with factual errors, logical errors, limited reasoning, and difficulty in understanding common sense knowledge, leading to issues such as semantic conflicts, semantic echoes, and denotational disambiguation during automatic human-like solving of mathematical application problems.
Innovation Solution
A system for semantic analysis and automatic solution of mathematical application problems, incorporating a semantic framework mode matching module, scenario semantic analysis module, data meta-variable naming module, and a module for explicitly expressing computation relationships, along with a machine thinking mechanism, to perform semantic inheritance and overloading, and implement automatic human-like solving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If chat models like ChatGPT are used for automatic solving of mathematical application problems, then the solving process appears human-like and flexible, but semantic conflicts, semantic echoes, and denotational disambiguation problems occur leading to factual errors and logical errors
Solution Approach 1:
The patent segments the solving process into distinct modules: a semantic analysis module that analyzes natural language semantics, a computation relationship expression module that formulates mathematical relationships, and a machine thinking module that performs logical deduction. This segmentation allows each module to specialize in its function, improving both the human-like quality and reliability of the solving process by eliminating semantic conflicts through structured analysis.
Solution Approach 2:
The patent introduces an intermediary semantic analysis module that acts as a bridge between natural language input and mathematical computation. This intermediary translates and disambiguates semantic meanings before they reach the computation module, preventing semantic echoes and denotational errors from propagating through the system, thus maintaining both human-like interpretation and computational accuracy.
2Loss of information
If semantic framework mode matching is performed on clause vocabulary sequences, then local semantics are formally expressed, but the system complexity increases
Solution Approach 1:
The patent divides the semantic analysis into clause-level segmentation, where each clause vocabulary sequence is independently matched with semantic frameworks. This segmentation allows formal semantic expression to be achieved at a manageable granular level, reducing overall system complexity by breaking down the complex task into smaller, handleable units.
Solution Approach 2:
The patent employs universal semantic frameworks that can match multiple clause types and vocabulary sequences. These frameworks serve multiple functions: they express local semantics, identify computation relationships, and enable machine thinking. This universality reduces system complexity by avoiding the need for separate specialized components for each function.
3Adaptability or versatility
If semantic inheritance and overloading are implemented across scenarios, then global semantics are formally expressed and applied, but the processing complexity increases
Solution Approach 1:
The patent implements preliminary semantic labeling during the semantic analysis phase, where semantic frameworks are pre-matched with clause vocabulary sequences and inheritance relationships are pre-established. This preliminary action allows global semantics to be formally expressed before the actual solving process begins, reducing processing complexity during execution by having the semantic structure already in place.
Data Source
AI summary
The present disclosure provides a system for semantic analysis and automatic solution of a mathematical application problem. The system includes: a semantic framework mode matching module, configured to: perform mode matching on a clause vocabulary sequence with a semantic framework, to form local semantic information of the mathematical application problem; a scenario semantic analysis module, configured to: form, based on a local feature vocabulary string of a scenario, a global semantic feature vocabulary string of the scenario; a data meta-variable naming module, configured to: generate a global semantic name of a data meta-variable based on a local name of a variable; a module for explicitly expressing a computation relationship, configured to: explicitly express an explicit computation rule and an implicit computation rule between data meta-variables, to construct a dynamic semantic circle; and a module for implementing a machine thinking mechanism, configured to solve the questionable data meta-variable.